Josh Dillon, Last Revised January 2022
This notebook examines an individual antenna's performance over a whole season. This notebook parses information from each nightly rtp_summarynotebook (as saved to .csvs) and builds a table describing antenna performance. It also reproduces per-antenna plots from each auto_metrics notebook pertinent to the specific antenna.
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "004"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder
# Use environment variables to figure out path to the csvs and auto_metrics
antenna = str(int(os.environ["ANTENNA"]))
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')
antenna = "182" csv_folder = "/home/obs/src/H6C_Notebooks/_rtp_summary_" auto_metrics_folder = "/home/obs/src/H6C_Notebooks/auto_metrics_inspect"
display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna
# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, 'rtp_summary_table*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')
Found 24 csvs in /home/obs/src/H6C_Notebooks/_rtp_summary_ Found 24 auto_metrics notebooks in /home/obs/src/H6C_Notebooks/auto_metrics_inspect
# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0
def jd_to_summary_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'
def jd_to_auto_metrics_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'
this_antenna = None
jds = []
# parse information about antennas and nodes
for csv in csvs:
df = pd.read_csv(csv)
for n in range(len(df)):
# Add this day to the antenna
row = df.loc[n]
if isinstance(row['Ant'], str) and '<a href' in row['Ant']:
antnum = int(row['Ant'].split('</a>')[0].split('>')[-1]) # it's a link, extract antnum
else:
antnum = int(row['Ant'])
if antnum != int(antenna):
continue
if np.issubdtype(type(row['Node']), np.integer):
row['Node'] = str(row['Node'])
if type(row['Node']) == str and row['Node'].isnumeric():
row['Node'] = 'N' + ('0' if len(row['Node']) == 1 else '') + row['Node']
if this_antenna is None:
this_antenna = Antenna(row['Ant'], row['Node'])
jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
jds.append(jd)
this_antenna.add_day(jd, row)
break
# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]
df = pd.DataFrame(to_show)
# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
df[col] = bar_cols[col]
z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
df[col] = z_score_cols[col]
ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
df[col] = ant_metrics_cols[col]
redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]
for col in redcal_cols:
df[col] = redcal_cols[col]
# style dataframe
table = df.style.hide_index()\
.applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
.background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
.background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
.applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
.format({col: '{:,.4f}'.format for col in z_score_cols}) \
.format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
.format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
.set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])
This table reproduces each night's row for this antenna from the RTP Summary notebooks. For more info on the columns, see those notebooks, linked in the JD column.
display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))
| JDs | A Priori Status | Auto Metrics Flags | Dead Fraction in Ant Metrics (Jee) | Dead Fraction in Ant Metrics (Jnn) | Crossed Fraction in Ant Metrics | Flag Fraction Before Redcal | Flagged By Redcal chi^2 Fraction | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | Average Dead Ant Metric (Jee) | Average Dead Ant Metric (Jnn) | Average Crossed Ant Metric | Median chi^2 Per Antenna (Jee) | Median chi^2 Per Antenna (Jnn) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2460007 | digital_ok | 100.00% | 0.00% | 100.00% | 0.00% | - | - | 0.052333 | 13.607097 | -0.953733 | 11.687916 | -0.379283 | 7.247762 | 6.169762 | 2.937926 | 0.6434 | 0.0522 | 0.4679 | nan | nan |
| 2459999 | digital_ok | 0.00% | 98.41% | 98.91% | 0.00% | - | - | nan | nan | nan | nan | nan | nan | nan | nan | 0.3312 | 0.3248 | 0.2650 | nan | nan |
| 2459998 | digital_ok | 100.00% | 0.00% | 100.00% | 0.00% | - | - | 0.050719 | 11.469928 | -0.823364 | 9.881916 | -0.377191 | 10.301299 | 10.201294 | 2.470228 | 0.6261 | 0.0453 | 0.4820 | nan | nan |
| 2459997 | digital_ok | 100.00% | 0.00% | 100.00% | 0.00% | - | - | -0.071774 | 12.525558 | -0.573272 | 10.629936 | -0.537573 | 9.645299 | 11.960757 | 3.784031 | 0.6350 | 0.0507 | 0.4975 | nan | nan |
| 2459996 | digital_ok | 100.00% | 0.00% | 100.00% | 0.00% | - | - | 0.191453 | 13.534459 | 0.473986 | 13.031173 | 5.912832 | 9.308372 | 0.655310 | 1.447257 | 0.6471 | 0.0506 | 0.4929 | nan | nan |
| 2459995 | digital_ok | 100.00% | 0.00% | 100.00% | 0.00% | - | - | 0.173471 | 13.680941 | -0.670386 | 12.222810 | 8.981446 | 9.569925 | 2.586985 | 1.366704 | 0.6353 | 0.0547 | 0.4772 | nan | nan |
| 2459994 | digital_ok | 100.00% | 0.00% | 100.00% | 0.00% | - | - | 0.157897 | 13.259359 | -1.166283 | 10.703074 | -0.001583 | 9.611869 | 3.588806 | 2.565900 | 0.6285 | 0.0471 | 0.4753 | nan | nan |
| 2459993 | digital_ok | 100.00% | 0.00% | 98.92% | 0.00% | - | - | 0.060669 | 12.471974 | -1.016151 | 9.909228 | -0.138953 | 10.992488 | 2.884272 | 2.315954 | 0.6132 | 0.0410 | 0.4650 | nan | nan |
| 2459991 | digital_ok | 100.00% | 0.00% | 100.00% | 0.00% | - | - | 0.230627 | 15.443711 | -1.155353 | 10.489422 | 0.989484 | 10.820878 | 4.077660 | 0.915330 | 0.6412 | 0.0452 | 0.4966 | nan | nan |
| 2459990 | digital_ok | 100.00% | 0.00% | 100.00% | 0.00% | - | - | 0.153064 | 12.706613 | -1.281780 | 10.183682 | -0.062419 | 11.048026 | 5.246792 | 1.165562 | 0.6379 | 0.0485 | 0.4866 | nan | nan |
| 2459989 | digital_ok | 100.00% | 97.30% | 97.51% | 0.00% | - | - | 245.978957 | 246.527131 | inf | inf | 2963.635450 | 2954.900643 | 4761.498448 | 4772.964562 | 0.5080 | 0.4437 | 0.3070 | nan | nan |
| 2459988 | digital_ok | 100.00% | 0.00% | 100.00% | 0.00% | - | - | 0.345567 | 15.088415 | -1.360423 | 10.465418 | -0.700934 | 13.261314 | 3.857661 | 0.722414 | 0.6335 | 0.0439 | 0.4857 | nan | nan |
| 2459987 | digital_ok | 100.00% | 0.00% | 100.00% | 0.00% | - | - | 0.169927 | 12.638774 | -1.381799 | 10.345162 | 15.496596 | 8.017265 | 4.431810 | 2.594379 | 0.6407 | 0.0486 | 0.4924 | nan | nan |
| 2459986 | digital_ok | 100.00% | 0.00% | 100.00% | 0.00% | - | - | 0.483274 | 15.499530 | -1.105219 | 11.158024 | 3.626443 | 11.316802 | 2.595480 | 9.886906 | 0.6581 | 0.0465 | 0.4790 | nan | nan |
| 2459985 | digital_ok | 100.00% | 0.00% | 100.00% | 0.00% | - | - | 0.434438 | 14.017117 | -0.872422 | 10.400560 | 11.584559 | 8.657785 | 10.414465 | 2.287985 | 0.6418 | 0.0469 | 0.4946 | nan | nan |
| 2459984 | digital_ok | 100.00% | 0.00% | 98.32% | 0.00% | - | - | 0.250117 | 13.486348 | 0.263684 | 10.784678 | 3.021609 | 12.121424 | -0.904084 | 3.598452 | 0.6559 | 0.0551 | 0.4914 | nan | nan |
| 2459983 | digital_ok | 100.00% | 0.00% | 100.00% | 0.00% | - | - | 0.152347 | 13.174448 | 0.093819 | 10.188555 | 3.348273 | 11.211746 | 0.335167 | 6.314968 | 0.6674 | 0.0504 | 0.4806 | nan | nan |
| 2459982 | digital_ok | 100.00% | 0.00% | 100.00% | 0.00% | - | - | 0.134890 | 10.510283 | -0.629934 | 8.697161 | 0.182323 | 5.315459 | 0.339387 | 3.238989 | 0.7146 | 0.0470 | 0.5011 | nan | nan |
| 2459981 | digital_ok | 100.00% | 0.00% | 100.00% | 0.00% | - | - | 0.287902 | 12.106894 | -1.153003 | 10.829090 | 0.229163 | 12.422410 | 7.402290 | 1.130681 | 0.6411 | 0.0482 | 0.4902 | nan | nan |
| 2459980 | digital_ok | 100.00% | 0.00% | 100.00% | 0.00% | - | - | 0.029169 | 11.695642 | -1.155232 | 9.907259 | 0.255774 | 10.848173 | 0.000658 | 5.352528 | 0.6817 | 0.0488 | 0.4950 | nan | nan |
| 2459979 | digital_ok | 100.00% | 0.00% | 100.00% | 0.00% | - | - | 0.131346 | 12.170079 | -1.154140 | 9.257439 | -0.326416 | 10.156395 | 6.903089 | 0.769786 | 0.6336 | 0.0441 | 0.4871 | nan | nan |
| 2459978 | digital_ok | 100.00% | 0.00% | 100.00% | 0.00% | - | - | 0.199564 | 12.369913 | -1.197971 | 9.966466 | -0.050003 | 11.023486 | 10.338439 | 1.523037 | 0.6341 | 0.0423 | 0.4946 | nan | nan |
| 2459977 | digital_ok | 100.00% | 0.00% | 100.00% | 0.00% | - | - | 0.284804 | 13.125412 | -1.109285 | 9.832856 | 0.085019 | 11.364589 | 6.427282 | 1.335939 | 0.6006 | 0.0495 | 0.4545 | nan | nan |
| 2459976 | digital_ok | 100.00% | 0.00% | 100.00% | 0.00% | - | - | 0.294096 | 12.606954 | -1.147543 | 10.234781 | -0.578666 | 10.918255 | 7.012695 | 1.303232 | 0.6403 | 0.0439 | 0.4926 | nan | nan |
auto_metrics notebooks.¶htmls_to_display = []
for am_html in auto_metric_htmls:
html_to_display = ''
# read html into a list of lines
with open(am_html) as f:
lines = f.readlines()
# find section with this antenna's metric plots and add to html_to_display
jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
try:
section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
except ValueError:
continue
html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
for line in lines[section_start_line + 1:]:
html_to_display += line
if '<hr' in line:
htmls_to_display.append(html_to_display)
break
These figures are reproduced from auto_metrics notebooks. For more info on the specific plots and metrics, see those notebooks (linked at the JD). The most recent 100 days (at most) are shown.
for i, html_to_display in enumerate(htmls_to_display):
if i == 100:
break
display(HTML(html_to_display))
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 182 | N13 | digital_ok | nn Shape | 13.607097 | 0.052333 | 13.607097 | -0.953733 | 11.687916 | -0.379283 | 7.247762 | 6.169762 | 2.937926 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 182 | N13 | digital_ok | nn Shape | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 182 | N13 | digital_ok | nn Shape | 11.469928 | 0.050719 | 11.469928 | -0.823364 | 9.881916 | -0.377191 | 10.301299 | 10.201294 | 2.470228 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 182 | N13 | digital_ok | nn Shape | 12.525558 | -0.071774 | 12.525558 | -0.573272 | 10.629936 | -0.537573 | 9.645299 | 11.960757 | 3.784031 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 182 | N13 | digital_ok | nn Shape | 13.534459 | 0.191453 | 13.534459 | 0.473986 | 13.031173 | 5.912832 | 9.308372 | 0.655310 | 1.447257 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 182 | N13 | digital_ok | nn Shape | 13.680941 | 0.173471 | 13.680941 | -0.670386 | 12.222810 | 8.981446 | 9.569925 | 2.586985 | 1.366704 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 182 | N13 | digital_ok | nn Shape | 13.259359 | 0.157897 | 13.259359 | -1.166283 | 10.703074 | -0.001583 | 9.611869 | 3.588806 | 2.565900 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 182 | N13 | digital_ok | nn Shape | 12.471974 | 0.060669 | 12.471974 | -1.016151 | 9.909228 | -0.138953 | 10.992488 | 2.884272 | 2.315954 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 182 | N13 | digital_ok | nn Shape | 15.443711 | 0.230627 | 15.443711 | -1.155353 | 10.489422 | 0.989484 | 10.820878 | 4.077660 | 0.915330 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 182 | N13 | digital_ok | nn Shape | 12.706613 | 12.706613 | 0.153064 | 10.183682 | -1.281780 | 11.048026 | -0.062419 | 1.165562 | 5.246792 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 182 | N13 | digital_ok | nn Power | inf | 246.527131 | 245.978957 | inf | inf | 2954.900643 | 2963.635450 | 4772.964562 | 4761.498448 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 182 | N13 | digital_ok | nn Shape | 15.088415 | 15.088415 | 0.345567 | 10.465418 | -1.360423 | 13.261314 | -0.700934 | 0.722414 | 3.857661 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 182 | N13 | digital_ok | ee Temporal Variability | 15.496596 | 0.169927 | 12.638774 | -1.381799 | 10.345162 | 15.496596 | 8.017265 | 4.431810 | 2.594379 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 182 | N13 | digital_ok | nn Shape | 15.499530 | 15.499530 | 0.483274 | 11.158024 | -1.105219 | 11.316802 | 3.626443 | 9.886906 | 2.595480 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 182 | N13 | digital_ok | nn Shape | 14.017117 | 14.017117 | 0.434438 | 10.400560 | -0.872422 | 8.657785 | 11.584559 | 2.287985 | 10.414465 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 182 | N13 | digital_ok | nn Shape | 13.486348 | 0.250117 | 13.486348 | 0.263684 | 10.784678 | 3.021609 | 12.121424 | -0.904084 | 3.598452 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 182 | N13 | digital_ok | nn Shape | 13.174448 | 0.152347 | 13.174448 | 0.093819 | 10.188555 | 3.348273 | 11.211746 | 0.335167 | 6.314968 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 182 | N13 | digital_ok | nn Shape | 10.510283 | 0.134890 | 10.510283 | -0.629934 | 8.697161 | 0.182323 | 5.315459 | 0.339387 | 3.238989 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 182 | N13 | digital_ok | nn Temporal Variability | 12.422410 | 12.106894 | 0.287902 | 10.829090 | -1.153003 | 12.422410 | 0.229163 | 1.130681 | 7.402290 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 182 | N13 | digital_ok | nn Shape | 11.695642 | 11.695642 | 0.029169 | 9.907259 | -1.155232 | 10.848173 | 0.255774 | 5.352528 | 0.000658 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 182 | N13 | digital_ok | nn Shape | 12.170079 | 0.131346 | 12.170079 | -1.154140 | 9.257439 | -0.326416 | 10.156395 | 6.903089 | 0.769786 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 182 | N13 | digital_ok | nn Shape | 12.369913 | 12.369913 | 0.199564 | 9.966466 | -1.197971 | 11.023486 | -0.050003 | 1.523037 | 10.338439 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 182 | N13 | digital_ok | nn Shape | 13.125412 | 0.284804 | 13.125412 | -1.109285 | 9.832856 | 0.085019 | 11.364589 | 6.427282 | 1.335939 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 182 | N13 | digital_ok | nn Shape | 12.606954 | 12.606954 | 0.294096 | 10.234781 | -1.147543 | 10.918255 | -0.578666 | 1.303232 | 7.012695 |